{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "tick价差套利（参考vnpy网友资料、vnpy论坛资料、windquant）：\n",
    "1、按被动腿时间戳对齐\n",
    "2、profile函数展示\n",
    "3、平稳性检验\n",
    "4、对冲手数计算\n",
    "5、2sigma开仓，3sigma止损(或者赌价差扩散？)，连续止损后cool down一段时间"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-13T19:15:03.864000Z",
     "start_time": "2019-08-13T19:15:03.609000Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lastPrice_x</th>\n",
       "      <th>volume_x</th>\n",
       "      <th>openInterest_x</th>\n",
       "      <th>openInterestYd_x</th>\n",
       "      <th>openPrice_x</th>\n",
       "      <th>highPrice_x</th>\n",
       "      <th>lowPrice_x</th>\n",
       "      <th>notionalTrade_x</th>\n",
       "      <th>lastPrice_y</th>\n",
       "      <th>volume_y</th>\n",
       "      <th>openInterest_y</th>\n",
       "      <th>openInterestYd_y</th>\n",
       "      <th>openPrice_y</th>\n",
       "      <th>highPrice_y</th>\n",
       "      <th>lowPrice_y</th>\n",
       "      <th>notionalTrade_y</th>\n",
       "      <th>rb1910_minus_rb2001</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>43329.000000</td>\n",
       "      <td>4.332900e+04</td>\n",
       "      <td>4.332900e+04</td>\n",
       "      <td>43329.0</td>\n",
       "      <td>43329.0</td>\n",
       "      <td>43329.000000</td>\n",
       "      <td>43329.000000</td>\n",
       "      <td>4.332900e+04</td>\n",
       "      <td>43329.000000</td>\n",
       "      <td>39968.000000</td>\n",
       "      <td>39968.000000</td>\n",
       "      <td>39968.0</td>\n",
       "      <td>39968.0</td>\n",
       "      <td>39968.000000</td>\n",
       "      <td>39968.000000</td>\n",
       "      <td>3.996800e+04</td>\n",
       "      <td>43329.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3760.000785</td>\n",
       "      <td>1.717720e+06</td>\n",
       "      <td>2.337862e+06</td>\n",
       "      <td>2345822.0</td>\n",
       "      <td>3760.0</td>\n",
       "      <td>3782.900990</td>\n",
       "      <td>3729.432805</td>\n",
       "      <td>6.463322e+10</td>\n",
       "      <td>3485.225023</td>\n",
       "      <td>79026.820606</td>\n",
       "      <td>306339.890913</td>\n",
       "      <td>297292.0</td>\n",
       "      <td>3465.0</td>\n",
       "      <td>3499.725330</td>\n",
       "      <td>3446.008782</td>\n",
       "      <td>2.755686e+09</td>\n",
       "      <td>274.775762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11.249415</td>\n",
       "      <td>7.254951e+05</td>\n",
       "      <td>3.044384e+04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.307426</td>\n",
       "      <td>2.671882</td>\n",
       "      <td>2.724721e+10</td>\n",
       "      <td>10.192081</td>\n",
       "      <td>35540.193744</td>\n",
       "      <td>7137.852168</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.486671</td>\n",
       "      <td>0.384799</td>\n",
       "      <td>1.238544e+09</td>\n",
       "      <td>3.062824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3725.000000</td>\n",
       "      <td>2.561400e+04</td>\n",
       "      <td>2.281840e+06</td>\n",
       "      <td>2345822.0</td>\n",
       "      <td>3760.0</td>\n",
       "      <td>3761.000000</td>\n",
       "      <td>3725.000000</td>\n",
       "      <td>9.630450e+08</td>\n",
       "      <td>3449.000000</td>\n",
       "      <td>1144.000000</td>\n",
       "      <td>296348.000000</td>\n",
       "      <td>297292.0</td>\n",
       "      <td>3465.0</td>\n",
       "      <td>3480.000000</td>\n",
       "      <td>3446.000000</td>\n",
       "      <td>3.964126e+07</td>\n",
       "      <td>265.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3752.000000</td>\n",
       "      <td>1.189468e+06</td>\n",
       "      <td>2.309758e+06</td>\n",
       "      <td>2345822.0</td>\n",
       "      <td>3760.0</td>\n",
       "      <td>3783.000000</td>\n",
       "      <td>3725.000000</td>\n",
       "      <td>4.479229e+10</td>\n",
       "      <td>3477.000000</td>\n",
       "      <td>49826.000000</td>\n",
       "      <td>298332.000000</td>\n",
       "      <td>297292.0</td>\n",
       "      <td>3465.0</td>\n",
       "      <td>3500.000000</td>\n",
       "      <td>3446.000000</td>\n",
       "      <td>1.737787e+09</td>\n",
       "      <td>273.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3763.000000</td>\n",
       "      <td>1.644332e+06</td>\n",
       "      <td>2.336918e+06</td>\n",
       "      <td>2345822.0</td>\n",
       "      <td>3760.0</td>\n",
       "      <td>3783.000000</td>\n",
       "      <td>3731.000000</td>\n",
       "      <td>6.192997e+10</td>\n",
       "      <td>3489.000000</td>\n",
       "      <td>80276.000000</td>\n",
       "      <td>306688.000000</td>\n",
       "      <td>297292.0</td>\n",
       "      <td>3465.0</td>\n",
       "      <td>3500.000000</td>\n",
       "      <td>3446.000000</td>\n",
       "      <td>2.801623e+09</td>\n",
       "      <td>275.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3769.000000</td>\n",
       "      <td>2.385754e+06</td>\n",
       "      <td>2.366494e+06</td>\n",
       "      <td>2345822.0</td>\n",
       "      <td>3760.0</td>\n",
       "      <td>3783.000000</td>\n",
       "      <td>3731.000000</td>\n",
       "      <td>8.970943e+10</td>\n",
       "      <td>3493.000000</td>\n",
       "      <td>110714.500000</td>\n",
       "      <td>313480.000000</td>\n",
       "      <td>297292.0</td>\n",
       "      <td>3465.0</td>\n",
       "      <td>3500.000000</td>\n",
       "      <td>3446.000000</td>\n",
       "      <td>3.859789e+09</td>\n",
       "      <td>277.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3783.000000</td>\n",
       "      <td>3.040168e+06</td>\n",
       "      <td>2.403682e+06</td>\n",
       "      <td>2345822.0</td>\n",
       "      <td>3760.0</td>\n",
       "      <td>3783.000000</td>\n",
       "      <td>3758.000000</td>\n",
       "      <td>1.142625e+11</td>\n",
       "      <td>3500.000000</td>\n",
       "      <td>139254.000000</td>\n",
       "      <td>317252.000000</td>\n",
       "      <td>297292.0</td>\n",
       "      <td>3465.0</td>\n",
       "      <td>3500.000000</td>\n",
       "      <td>3465.000000</td>\n",
       "      <td>4.851967e+09</td>\n",
       "      <td>293.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        lastPrice_x      volume_x  openInterest_x  openInterestYd_x  \\\n",
       "count  43329.000000  4.332900e+04    4.332900e+04           43329.0   \n",
       "mean    3760.000785  1.717720e+06    2.337862e+06         2345822.0   \n",
       "std       11.249415  7.254951e+05    3.044384e+04               0.0   \n",
       "min     3725.000000  2.561400e+04    2.281840e+06         2345822.0   \n",
       "25%     3752.000000  1.189468e+06    2.309758e+06         2345822.0   \n",
       "50%     3763.000000  1.644332e+06    2.336918e+06         2345822.0   \n",
       "75%     3769.000000  2.385754e+06    2.366494e+06         2345822.0   \n",
       "max     3783.000000  3.040168e+06    2.403682e+06         2345822.0   \n",
       "\n",
       "       openPrice_x   highPrice_x    lowPrice_x  notionalTrade_x   lastPrice_y  \\\n",
       "count      43329.0  43329.000000  43329.000000     4.332900e+04  43329.000000   \n",
       "mean        3760.0   3782.900990   3729.432805     6.463322e+10   3485.225023   \n",
       "std            0.0      1.307426      2.671882     2.724721e+10     10.192081   \n",
       "min         3760.0   3761.000000   3725.000000     9.630450e+08   3449.000000   \n",
       "25%         3760.0   3783.000000   3725.000000     4.479229e+10   3477.000000   \n",
       "50%         3760.0   3783.000000   3731.000000     6.192997e+10   3489.000000   \n",
       "75%         3760.0   3783.000000   3731.000000     8.970943e+10   3493.000000   \n",
       "max         3760.0   3783.000000   3758.000000     1.142625e+11   3500.000000   \n",
       "\n",
       "            volume_y  openInterest_y  openInterestYd_y  openPrice_y  \\\n",
       "count   39968.000000    39968.000000           39968.0      39968.0   \n",
       "mean    79026.820606   306339.890913          297292.0       3465.0   \n",
       "std     35540.193744     7137.852168               0.0          0.0   \n",
       "min      1144.000000   296348.000000          297292.0       3465.0   \n",
       "25%     49826.000000   298332.000000          297292.0       3465.0   \n",
       "50%     80276.000000   306688.000000          297292.0       3465.0   \n",
       "75%    110714.500000   313480.000000          297292.0       3465.0   \n",
       "max    139254.000000   317252.000000          297292.0       3465.0   \n",
       "\n",
       "        highPrice_y    lowPrice_y  notionalTrade_y  rb1910_minus_rb2001  \n",
       "count  39968.000000  39968.000000     3.996800e+04         43329.000000  \n",
       "mean    3499.725330   3446.008782     2.755686e+09           274.775762  \n",
       "std        1.486671      0.384799     1.238544e+09             3.062824  \n",
       "min     3480.000000   3446.000000     3.964126e+07           265.000000  \n",
       "25%     3500.000000   3446.000000     1.737787e+09           273.000000  \n",
       "50%     3500.000000   3446.000000     2.801623e+09           275.000000  \n",
       "75%     3500.000000   3446.000000     3.859789e+09           277.000000  \n",
       "max     3500.000000   3465.000000     4.851967e+09           293.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import DataFrame, Series\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "准备价差数据\n",
    "\"\"\"\n",
    "\n",
    "names = ['time','lastPrice','volume','openInterest','openInterestYd','openPrice','highPrice','lowPrice','notionalTrade']\n",
    "rb1910 = pd.read_csv(\"D:\\\\tick_mean_reversion\\\\rb1910.csv\", names=names)\n",
    "rb2001 = pd.read_csv(\"D:\\\\tick_mean_reversion\\\\rb2001.csv\", names=names)\n",
    "\n",
    "# rb1910 = rb1910.set_index('time')\n",
    "# rb1910.index = pd.DatetimeIndex(rb1910.index)\n",
    "rb1910['time'] = pd.to_datetime(rb1910['time'])\n",
    "rb2001['time'] = pd.to_datetime(rb2001['time'])\n",
    "# rb2001 = rb2001.set_index('time')\n",
    "# rb2001.index = pd.DatetimeIndex(rb2001.index)\n",
    "\n",
    "priceSpread = pd.merge(rb1910,rb2001,on='time',how='outer')\n",
    "priceSpread = priceSpread.set_index('time')\n",
    "priceSpread['lastPrice_y'].fillna(method='ffill',inplace=True)\n",
    "priceSpread['rb1910_minus_rb2001'] = priceSpread['lastPrice_x'] - priceSpread['lastPrice_y']\n",
    "priceSpread = priceSpread['2019-5-6  9:00:00':'2019-5-6  15:00:00']\n",
    "priceSpread.describe()\n",
    "\n",
    "# priceSpread.to_csv(\"D:\\\\tick_mean_reversion\\\\priceSpread.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-13T19:08:41.329000Z",
     "start_time": "2019-08-13T19:08:41.146000Z"
    }
   },
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "module compiled against API version 0xc but this version of numpy is 0xa",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[1;31mRuntimeError\u001b[0m: module compiled against API version 0xc but this version of numpy is 0xa"
     ]
    },
    {
     "ename": "ImportError",
     "evalue": "numpy.core.multiarray failed to import",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-3-dc789682db43>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mstatsmodels\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtsa\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstattools\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0madfuller\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mADF\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \"\"\"\n\u001b[0;32m      4\u001b[0m \u001b[0mrb\u001b[0m\u001b[0;31m主\u001b[0m\u001b[0;31m力\u001b[0m\u001b[0;31m和\u001b[0m\u001b[0;31m次\u001b[0m\u001b[0;31m主\u001b[0m\u001b[0;31m力\u001b[0m\u001b[0;31m合\u001b[0m\u001b[0;31m约\u001b[0m\u001b[0;31m的\u001b[0m\u001b[0;31m平\u001b[0m\u001b[0;31m稳\u001b[0m\u001b[0;31m性\u001b[0m\u001b[0;31m检\u001b[0m\u001b[0;31m验\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \"\"\"\n",
      "\u001b[1;32mD:\\Anaconda2\\lib\\site-packages\\statsmodels\\__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mwarnings\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0msimplefilter\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m from statsmodels.tools.sm_exceptions import (ConvergenceWarning, CacheWriteWarning,\n\u001b[0m\u001b[0;32m     11\u001b[0m                                              IterationLimitWarning, InvalidTestWarning)\n\u001b[0;32m     12\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda2\\lib\\site-packages\\statsmodels\\tools\\__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0mtools\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0madd_constant\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcategorical\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mD:\\Anaconda2\\lib\\site-packages\\statsmodels\\tools\\tools.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrecfunctions\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mnprf\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlinalg\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mL\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mscipy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlinalg\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0msvdvals\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      9\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda2\\lib\\site-packages\\scipy\\linalg\\__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m    188\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0mlinalg_version\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mlinalg_version\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0m__version__\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    189\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 190\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0mmisc\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[1;33m*\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    191\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0mbasic\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[1;33m*\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    192\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0mdecomp\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[1;33m*\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda2\\lib\\site-packages\\scipy\\linalg\\misc.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlinalg\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mLinAlgError\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0mblas\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mget_blas_funcs\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0mlapack\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mget_lapack_funcs\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda2\\lib\\site-packages\\scipy\\linalg\\blas.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m    212\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0m_np\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    213\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 214\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mscipy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlinalg\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0m_fblas\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    215\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    216\u001b[0m     \u001b[1;32mfrom\u001b[0m \u001b[0mscipy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlinalg\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0m_cblas\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mImportError\u001b[0m: numpy.core.multiarray failed to import"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.stattools import adfuller as ADF\n",
    "\n",
    "\"\"\"\n",
    "rb主力和次主力合约的平稳性检验\n",
    "\"\"\"\n",
    "\n",
    "ret_rb1910 = rb1910['lastPrice']/rb1910['lastPrice'].shift(1).apply(lambda x:np.log(x))\n",
    "ret_rb2001 = rb2001['lastPrice']/rb2001['lastPrice'].shift(1).apply(lambda x:np.log(x))\n",
    "\n",
    "tt = ADF((ret_rb1910 - ret_rb2001).dropna()) #单个配对稳定性检验\n",
    "\n",
    "output=pd.DataFrame(index=['Test Statistic Value', \"p-value\", \"Lags Used\", \"Number of Observations Used\",\"Critical Value(1%)\",\"Critical Value(5%)\",\"Critical Value(10%)\"],columns=['Y-value'])\n",
    "output['Y-value']['Test Statistic Value'] = tt[0]\n",
    "output['Y-value']['p-value'] = tt[1]\n",
    "output['Y-value']['Lags Used'] = tt[2]\n",
    "output['Y-value']['Number of Observations Used'] = tt[3]\n",
    "output['Y-value']['Critical Value(1%)'] = tt[4]['1%']\n",
    "output['Y-value']['Critical Value(5%)'] = tt[4]['5%']\n",
    "output['Y-value']['Critical Value(10%)'] = tt[4]['10%']\n",
    "output.columns.name = 'result of rb1910 vs rb2001'\n",
    "print (\"p-value of rb1910 vs rb2001: \",tt[1])\n",
    "output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-13T19:09:28.139000Z",
     "start_time": "2019-08-13T19:09:27.677000Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc05b030>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pylab as plt\n",
    "\n",
    "\"\"\"\n",
    "画图观察全天价差的波动范围\n",
    "\"\"\"\n",
    "band_multi = 2 #设定上下轨偏离标准差多少倍\n",
    "\n",
    "intraDayMean = priceSpread['rb1910_minus_rb2001'].mean()\n",
    "intraDayStd = priceSpread['rb1910_minus_rb2001'].std()\n",
    "fig = plt.figure(figsize=(21,12))\n",
    "priceSpread['rb1910_minus_rb2001'].plot(color='#407CE2')\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "plt.axhline(intraDayMean, color=\"black\")\n",
    "ax.axhline(intraDayMean+band_multi*intraDayStd, color=\"red\", linestyle=\"--\")\n",
    "ax.axhline(intraDayMean-band_multi*intraDayStd, color=\"green\", linestyle=\"--\")\n",
    "plt.xlabel(\"Time\"); plt.ylabel(\"IntraDay Price spread of rb1910 and rb2001\")\n",
    "plt.legend([\"Price spread\", \"Mean\",\"Upper_band\",\"Lower_band\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-13T19:09:31.951000Z",
     "start_time": "2019-08-13T19:09:31.134000Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x93612b0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "计算滚动波动范围\n",
    "\"\"\"\n",
    "band_multi = 2 \n",
    "\n",
    "mean = priceSpread['rb1910_minus_rb2001'].rolling(window=1200).mean()\n",
    "std = priceSpread['rb1910_minus_rb2001'].rolling(window=1200).std()\n",
    "\n",
    "pd.concat([priceSpread['rb1910_minus_rb2001'],mean, mean+band_multi*std,mean-band_multi*std], axis=1).plot(figsize=(21,12))\n",
    "plt.xlabel(\"Time\"); plt.ylabel(\"Rolling bands of spread\")\n",
    "plt.legend([\"Price spread\", \"Rolling Mean\",\"Rolling Upper_band\",\"Rolling Lower_band\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-13T19:16:03.909000Z",
     "start_time": "2019-08-13T19:16:03.781000Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lastPrice_x</th>\n",
       "      <th>volume_x</th>\n",
       "      <th>openInterest_x</th>\n",
       "      <th>openInterestYd_x</th>\n",
       "      <th>openPrice_x</th>\n",
       "      <th>highPrice_x</th>\n",
       "      <th>lowPrice_x</th>\n",
       "      <th>notionalTrade_x</th>\n",
       "      <th>lastPrice_y</th>\n",
       "      <th>volume_y</th>\n",
       "      <th>...</th>\n",
       "      <th>openPrice_y</th>\n",
       "      <th>highPrice_y</th>\n",
       "      <th>lowPrice_y</th>\n",
       "      <th>notionalTrade_y</th>\n",
       "      <th>rb1910_minus_rb2001</th>\n",
       "      <th>Rolling_Upper_band</th>\n",
       "      <th>Rolling_Lower_band</th>\n",
       "      <th>LongHolding</th>\n",
       "      <th>ShortHolding</th>\n",
       "      <th>Position</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-05-06 14:59:58</th>\n",
       "      <td>3756.0</td>\n",
       "      <td>3039446</td>\n",
       "      <td>2306284.0</td>\n",
       "      <td>2345822.0</td>\n",
       "      <td>3760.0</td>\n",
       "      <td>3783.0</td>\n",
       "      <td>3725.0</td>\n",
       "      <td>1.142353e+11</td>\n",
       "      <td>3483.0</td>\n",
       "      <td>139244.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3465.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>3446.0</td>\n",
       "      <td>4.851618e+09</td>\n",
       "      <td>273.0</td>\n",
       "      <td>279.574538</td>\n",
       "      <td>271.803795</td>\n",
       "      <td>-6.574538</td>\n",
       "      <td>1.196205</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-06 14:59:58</th>\n",
       "      <td>3756.0</td>\n",
       "      <td>3039446</td>\n",
       "      <td>2306284.0</td>\n",
       "      <td>2345822.0</td>\n",
       "      <td>3760.0</td>\n",
       "      <td>3783.0</td>\n",
       "      <td>3725.0</td>\n",
       "      <td>1.142353e+11</td>\n",
       "      <td>3483.0</td>\n",
       "      <td>139244.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3465.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>3446.0</td>\n",
       "      <td>4.851618e+09</td>\n",
       "      <td>273.0</td>\n",
       "      <td>279.571174</td>\n",
       "      <td>271.798826</td>\n",
       "      <td>-6.571174</td>\n",
       "      <td>1.201174</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-06 14:59:58</th>\n",
       "      <td>3756.0</td>\n",
       "      <td>3039670</td>\n",
       "      <td>2306106.0</td>\n",
       "      <td>2345822.0</td>\n",
       "      <td>3760.0</td>\n",
       "      <td>3783.0</td>\n",
       "      <td>3725.0</td>\n",
       "      <td>1.142438e+11</td>\n",
       "      <td>3483.0</td>\n",
       "      <td>139244.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3465.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>3446.0</td>\n",
       "      <td>4.851618e+09</td>\n",
       "      <td>273.0</td>\n",
       "      <td>279.567793</td>\n",
       "      <td>271.793874</td>\n",
       "      <td>-6.567793</td>\n",
       "      <td>1.206126</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-06 14:59:58</th>\n",
       "      <td>3756.0</td>\n",
       "      <td>3039670</td>\n",
       "      <td>2306106.0</td>\n",
       "      <td>2345822.0</td>\n",
       "      <td>3760.0</td>\n",
       "      <td>3783.0</td>\n",
       "      <td>3725.0</td>\n",
       "      <td>1.142438e+11</td>\n",
       "      <td>3483.0</td>\n",
       "      <td>139244.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3465.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>3446.0</td>\n",
       "      <td>4.851618e+09</td>\n",
       "      <td>273.0</td>\n",
       "      <td>279.564393</td>\n",
       "      <td>271.788940</td>\n",
       "      <td>-6.564393</td>\n",
       "      <td>1.211060</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-06 14:59:59</th>\n",
       "      <td>3756.0</td>\n",
       "      <td>3039902</td>\n",
       "      <td>2305948.0</td>\n",
       "      <td>2345822.0</td>\n",
       "      <td>3760.0</td>\n",
       "      <td>3783.0</td>\n",
       "      <td>3725.0</td>\n",
       "      <td>1.142525e+11</td>\n",
       "      <td>3484.0</td>\n",
       "      <td>139250.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3465.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>3446.0</td>\n",
       "      <td>4.851827e+09</td>\n",
       "      <td>272.0</td>\n",
       "      <td>279.562863</td>\n",
       "      <td>271.780471</td>\n",
       "      <td>-7.562863</td>\n",
       "      <td>0.219529</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-06 14:59:59</th>\n",
       "      <td>3757.0</td>\n",
       "      <td>3040014</td>\n",
       "      <td>2305884.0</td>\n",
       "      <td>2345822.0</td>\n",
       "      <td>3760.0</td>\n",
       "      <td>3783.0</td>\n",
       "      <td>3725.0</td>\n",
       "      <td>1.142567e+11</td>\n",
       "      <td>3484.0</td>\n",
       "      <td>139250.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3465.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>3446.0</td>\n",
       "      <td>4.851827e+09</td>\n",
       "      <td>273.0</td>\n",
       "      <td>279.559423</td>\n",
       "      <td>271.775577</td>\n",
       "      <td>-6.559423</td>\n",
       "      <td>1.224423</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-06 15:00:00</th>\n",
       "      <td>3756.0</td>\n",
       "      <td>3040168</td>\n",
       "      <td>2305756.0</td>\n",
       "      <td>2345822.0</td>\n",
       "      <td>3760.0</td>\n",
       "      <td>3783.0</td>\n",
       "      <td>3725.0</td>\n",
       "      <td>1.142625e+11</td>\n",
       "      <td>3483.0</td>\n",
       "      <td>139254.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3465.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>3446.0</td>\n",
       "      <td>4.851967e+09</td>\n",
       "      <td>273.0</td>\n",
       "      <td>279.555965</td>\n",
       "      <td>271.770701</td>\n",
       "      <td>-6.555965</td>\n",
       "      <td>1.229299</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-06 15:00:00</th>\n",
       "      <td>3756.0</td>\n",
       "      <td>3040168</td>\n",
       "      <td>2305756.0</td>\n",
       "      <td>2345822.0</td>\n",
       "      <td>3760.0</td>\n",
       "      <td>3783.0</td>\n",
       "      <td>3725.0</td>\n",
       "      <td>1.142625e+11</td>\n",
       "      <td>3483.0</td>\n",
       "      <td>139254.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3465.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>3446.0</td>\n",
       "      <td>4.851967e+09</td>\n",
       "      <td>273.0</td>\n",
       "      <td>279.549221</td>\n",
       "      <td>271.767446</td>\n",
       "      <td>-6.549221</td>\n",
       "      <td>1.232554</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-06 15:00:00</th>\n",
       "      <td>3756.0</td>\n",
       "      <td>3040168</td>\n",
       "      <td>2305756.0</td>\n",
       "      <td>2345822.0</td>\n",
       "      <td>3760.0</td>\n",
       "      <td>3783.0</td>\n",
       "      <td>3725.0</td>\n",
       "      <td>1.142625e+11</td>\n",
       "      <td>3483.0</td>\n",
       "      <td>139254.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3465.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>3446.0</td>\n",
       "      <td>4.851967e+09</td>\n",
       "      <td>273.0</td>\n",
       "      <td>279.545724</td>\n",
       "      <td>271.762609</td>\n",
       "      <td>-6.545724</td>\n",
       "      <td>1.237391</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-06 15:00:00</th>\n",
       "      <td>3756.0</td>\n",
       "      <td>3040168</td>\n",
       "      <td>2305756.0</td>\n",
       "      <td>2345822.0</td>\n",
       "      <td>3760.0</td>\n",
       "      <td>3783.0</td>\n",
       "      <td>3725.0</td>\n",
       "      <td>1.142625e+11</td>\n",
       "      <td>3483.0</td>\n",
       "      <td>139254.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3465.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>3446.0</td>\n",
       "      <td>4.851967e+09</td>\n",
       "      <td>273.0</td>\n",
       "      <td>279.542209</td>\n",
       "      <td>271.757791</td>\n",
       "      <td>-6.542209</td>\n",
       "      <td>1.242209</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     lastPrice_x  volume_x  openInterest_x  openInterestYd_x  \\\n",
       "time                                                                           \n",
       "2019-05-06 14:59:58       3756.0   3039446       2306284.0         2345822.0   \n",
       "2019-05-06 14:59:58       3756.0   3039446       2306284.0         2345822.0   \n",
       "2019-05-06 14:59:58       3756.0   3039670       2306106.0         2345822.0   \n",
       "2019-05-06 14:59:58       3756.0   3039670       2306106.0         2345822.0   \n",
       "2019-05-06 14:59:59       3756.0   3039902       2305948.0         2345822.0   \n",
       "2019-05-06 14:59:59       3757.0   3040014       2305884.0         2345822.0   \n",
       "2019-05-06 15:00:00       3756.0   3040168       2305756.0         2345822.0   \n",
       "2019-05-06 15:00:00       3756.0   3040168       2305756.0         2345822.0   \n",
       "2019-05-06 15:00:00       3756.0   3040168       2305756.0         2345822.0   \n",
       "2019-05-06 15:00:00       3756.0   3040168       2305756.0         2345822.0   \n",
       "\n",
       "                     openPrice_x  highPrice_x  lowPrice_x  notionalTrade_x  \\\n",
       "time                                                                         \n",
       "2019-05-06 14:59:58       3760.0       3783.0      3725.0     1.142353e+11   \n",
       "2019-05-06 14:59:58       3760.0       3783.0      3725.0     1.142353e+11   \n",
       "2019-05-06 14:59:58       3760.0       3783.0      3725.0     1.142438e+11   \n",
       "2019-05-06 14:59:58       3760.0       3783.0      3725.0     1.142438e+11   \n",
       "2019-05-06 14:59:59       3760.0       3783.0      3725.0     1.142525e+11   \n",
       "2019-05-06 14:59:59       3760.0       3783.0      3725.0     1.142567e+11   \n",
       "2019-05-06 15:00:00       3760.0       3783.0      3725.0     1.142625e+11   \n",
       "2019-05-06 15:00:00       3760.0       3783.0      3725.0     1.142625e+11   \n",
       "2019-05-06 15:00:00       3760.0       3783.0      3725.0     1.142625e+11   \n",
       "2019-05-06 15:00:00       3760.0       3783.0      3725.0     1.142625e+11   \n",
       "\n",
       "                     lastPrice_y  volume_y    ...     openPrice_y  \\\n",
       "time                                          ...                   \n",
       "2019-05-06 14:59:58       3483.0  139244.0    ...          3465.0   \n",
       "2019-05-06 14:59:58       3483.0  139244.0    ...          3465.0   \n",
       "2019-05-06 14:59:58       3483.0  139244.0    ...          3465.0   \n",
       "2019-05-06 14:59:58       3483.0  139244.0    ...          3465.0   \n",
       "2019-05-06 14:59:59       3484.0  139250.0    ...          3465.0   \n",
       "2019-05-06 14:59:59       3484.0  139250.0    ...          3465.0   \n",
       "2019-05-06 15:00:00       3483.0  139254.0    ...          3465.0   \n",
       "2019-05-06 15:00:00       3483.0  139254.0    ...          3465.0   \n",
       "2019-05-06 15:00:00       3483.0  139254.0    ...          3465.0   \n",
       "2019-05-06 15:00:00       3483.0  139254.0    ...          3465.0   \n",
       "\n",
       "                     highPrice_y  lowPrice_y  notionalTrade_y  \\\n",
       "time                                                            \n",
       "2019-05-06 14:59:58       3500.0      3446.0     4.851618e+09   \n",
       "2019-05-06 14:59:58       3500.0      3446.0     4.851618e+09   \n",
       "2019-05-06 14:59:58       3500.0      3446.0     4.851618e+09   \n",
       "2019-05-06 14:59:58       3500.0      3446.0     4.851618e+09   \n",
       "2019-05-06 14:59:59       3500.0      3446.0     4.851827e+09   \n",
       "2019-05-06 14:59:59       3500.0      3446.0     4.851827e+09   \n",
       "2019-05-06 15:00:00       3500.0      3446.0     4.851967e+09   \n",
       "2019-05-06 15:00:00       3500.0      3446.0     4.851967e+09   \n",
       "2019-05-06 15:00:00       3500.0      3446.0     4.851967e+09   \n",
       "2019-05-06 15:00:00       3500.0      3446.0     4.851967e+09   \n",
       "\n",
       "                     rb1910_minus_rb2001  Rolling_Upper_band  \\\n",
       "time                                                           \n",
       "2019-05-06 14:59:58                273.0          279.574538   \n",
       "2019-05-06 14:59:58                273.0          279.571174   \n",
       "2019-05-06 14:59:58                273.0          279.567793   \n",
       "2019-05-06 14:59:58                273.0          279.564393   \n",
       "2019-05-06 14:59:59                272.0          279.562863   \n",
       "2019-05-06 14:59:59                273.0          279.559423   \n",
       "2019-05-06 15:00:00                273.0          279.555965   \n",
       "2019-05-06 15:00:00                273.0          279.549221   \n",
       "2019-05-06 15:00:00                273.0          279.545724   \n",
       "2019-05-06 15:00:00                273.0          279.542209   \n",
       "\n",
       "                     Rolling_Lower_band  LongHolding  ShortHolding  Position  \n",
       "time                                                                          \n",
       "2019-05-06 14:59:58          271.803795    -6.574538      1.196205       0.0  \n",
       "2019-05-06 14:59:58          271.798826    -6.571174      1.201174       0.0  \n",
       "2019-05-06 14:59:58          271.793874    -6.567793      1.206126       0.0  \n",
       "2019-05-06 14:59:58          271.788940    -6.564393      1.211060       0.0  \n",
       "2019-05-06 14:59:59          271.780471    -7.562863      0.219529       0.0  \n",
       "2019-05-06 14:59:59          271.775577    -6.559423      1.224423       0.0  \n",
       "2019-05-06 15:00:00          271.770701    -6.555965      1.229299       0.0  \n",
       "2019-05-06 15:00:00          271.767446    -6.549221      1.232554       0.0  \n",
       "2019-05-06 15:00:00          271.762609    -6.545724      1.237391       0.0  \n",
       "2019-05-06 15:00:00          271.757791    -6.542209      1.242209       0.0  \n",
       "\n",
       "[10 rows x 22 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "计算日内交易盈亏\n",
    "\"\"\"\n",
    "\n",
    "window=1200\n",
    "band_multi = 2 \n",
    "\n",
    "mean = priceSpread['rb1910_minus_rb2001'].rolling(window=window).mean()\n",
    "std = priceSpread['rb1910_minus_rb2001'].rolling(window=window).std()\n",
    "\n",
    "priceSpread['Rolling_Upper_band'] = mean + band_multi * std\n",
    "priceSpread['Rolling_Lower_band'] = mean - band_multi * std\n",
    "\n",
    "priceSpread['LongHolding'] = priceSpread['rb1910_minus_rb2001'] - priceSpread['Rolling_Upper_band']\n",
    "priceSpread['ShortHolding'] = priceSpread['rb1910_minus_rb2001'] - priceSpread['Rolling_Lower_band']\n",
    "priceSpread['Position'] = priceSpread['rb1910_minus_rb2001'] * 0\n",
    "\n",
    "priceSpread.tail(10)\n",
    "\n"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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